Collaboration, Interruptions, and Changeover Times: Workflow Model and Empirical Study of Hospitalist Charting
Bibliographic record
Abstract
Problem definition: Collaboration is important in services but may lead to interruptions. Professionals exercise discretion on when to preempt individual tasks to switch to collaborative tasks. Academic/practical relevance: Discretionary task switching can introduce changeover times when resuming the preempted task and, thus, can increase total processing time. Methodology: We analyze and quantify how collaboration, through interruptions and discretionary changeovers, affects total processing time. We introduce an episodal workflow model that captures the interruption and discretionary changeover dynamics—each switch and the episode of work it preempts—present in settings in which collaboration and multitasking is paramount. A simulation study provides evidence that changeover times are properly identified and estimated without bias. We then deploy the model in a field study of hospital medicine physicians: “hospitalists.” The hospitalist workflow includes visiting patients, consulting with other caregivers to guide patient diagnosis and treatment, and documenting in the patient’s medical chart. The empirical analysis uses a data set assembled from direct observation of hospitalist activity and pager-log data. Results: We estimate that a hospitalist incurs a total changeover time during documentation of five minutes per patient per day. Managerial implications: This estimate represents a significant 20% of the total processing time per patient: caring for 14 patients per day, our model estimates that a hospitalist spends more than one hour each day on changeovers. This provides evidence that task switching can causally lead to longer documentation time.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".